Simulation Study on Kalman Filter Application in Target Tracking

In target tracking applications, Kalman filter serves as a widely adopted technique. Research demonstrates that implementing Kalman filtering algorithms can significantly enhance tracking accuracy and stability. This study explores the practical application of Kalman filters in target tracking scenarios through comprehensive simulation analysis. The implementation typically involves key mathematical operations including state prediction (xₖ|ₖ₋₁ = Fxₖ₋₁|ₖ₋₁) and measurement update steps, with covariance matrix calculations ensuring optimal estimation. We provide complete simulation code with real-time visualization capabilities and video demonstrations, allowing readers to directly execute and observe the tracking performance. Through study and hands-on practice with this material, readers can deepen their understanding of Kalman filter principles, algorithmic implementation details, and practical applications, thereby providing robust support for research in the target tracking domain.